CostSensitiveClassifier

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.SingleClassifierEnhancer
  - - weka.classifiers.RandomizableSingleClassifierEnhancer
    - - weka.classifiers.meta.CostSensitiveClassifier

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, Drawable, OptionHandler, Randomizable, RevisionHandler, WeightedInstancesHandler

public class CostSensitiveClassifier
extends RandomizableSingleClassifierEnhancer
implements OptionHandler, Drawable, BatchPredictor, WeightedInstancesHandler

A metaclassifier that makes its base classifier cost sensitive. Two methods can be used to introduce cost-sensitivity: reweighting training instances according to the total cost assigned to each class; or predicting the class with minimum expected misclassification cost (rather than the most likely class). Performance can often be improved by using a bagged classifier to improve the probability estimates of the base classifier. If the base classifier cannot handle instance weights, and the instance weights are not uniform, the data will be resampled with replacement based on the weights before being passed to the base classifier.

Valid options are:

 -M
  Minimize expected misclassification cost. Default is to
  reweight training instances according to costs per class

 -C <cost file name>
  File name of a cost matrix to use. If this is not supplied,
  a cost matrix will be loaded on demand. The name of the
  on-demand file is the relation name of the training data
  plus ".cost", and the path to the on-demand file is
  specified with the -N option.

 -N <directory>
  Name of a directory to search for cost files when loading
  costs on demand (default current directory).

 -cost-matrix <matrix>
  The cost matrix in Matlab single line format.

 -S <num>
  Random number seed.
  (default 1)

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -W
  Full name of base classifier.
  (default: weka.classifiers.rules.ZeroR)

 
 Options specific to classifier weka.classifiers.rules.ZeroR:

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

Options after -- are passed to the designated classifier.

Version:: $Revision: 15520 $
Author:: Len Trigg (len@reeltwo.com)
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`MATRIX_ON_DEMAND` load cost matrix on demand
`static int`	`MATRIX_SUPPLIED` use explicit cost matrix
`static Tag[]`	`TAGS_MATRIX_SOURCE` Specify possible sources of the cost matrix

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Fields inherited from interface weka.core.Drawable
BayesNet, Newick, NOT_DRAWABLE, TREE

Constructor Summary

Constructors
Constructor and Description

CostSensitiveClassifier()
Default constructor.

Constructors
Constructor and Description
`CostSensitiveClassifier()` Default constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String`	`batchSizeTipText()` Tool tip text for this property
`void`	`buildClassifier(Instances data)` Builds the model of the base learner.
`java.lang.String`	`costMatrixSourceTipText()`
`java.lang.String`	`costMatrixTipText()`
`double[]`	`distributionForInstance(Instance instance)` Returns class probabilities.
`double[][]`	`distributionsForInstances(Instances insts)` Batch scoring method.
`java.lang.String`	`getBatchSize()` Gets the preferred batch size from the base learner if it implements BatchPredictor.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`CostMatrix`	`getCostMatrix()` Gets the misclassification cost matrix.
`SelectedTag`	`getCostMatrixSource()` Gets the source location method of the cost matrix.
`boolean`	`getMinimizeExpectedCost()` Gets the value of MinimizeExpectedCost.
`java.io.File`	`getOnDemandDirectory()` Returns the directory that will be searched for cost files when loading on demand.
`java.lang.String[]`	`getOptions()` Gets the current settings of the Classifier.
`java.lang.String`	`getRevision()` Returns the revision string.
`java.lang.String`	`globalInfo()`
`java.lang.String`	`graph()` Returns graph describing the classifier (if possible).
`int`	`graphType()` Returns the type of graph this classifier represents.
`boolean`	`implementsMoreEfficientBatchPrediction()` Returns true if the base classifier implements BatchPredictor and is able to generate batch predictions efficiently
`java.util.Enumeration<Option>`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`java.lang.String`	`minimizeExpectedCostTipText()`
`java.lang.String`	`onDemandDirectoryTipText()`
`void`	`setBatchSize(java.lang.String size)` Set the batch size to use.
`void`	`setCostMatrix(CostMatrix newCostMatrix)` Sets the misclassification cost matrix.
`void`	`setCostMatrixSource(SelectedTag newMethod)` Sets the source location of the cost matrix.
`void`	`setMinimizeExpectedCost(boolean newMinimizeExpectedCost)` Set the value of MinimizeExpectedCost.
`void`	`setOnDemandDirectory(java.io.File newDir)` Sets the directory that will be searched for cost files when loading on demand.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`java.lang.String`	`toString()` Output a representation of this classifier

Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer
getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier
classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Field Detail
  - MATRIX_ON_DEMAND
```
public static final int MATRIX_ON_DEMAND
```
    load cost matrix on demand
    
    See Also:
    
    Constant Field Values
  - MATRIX_SUPPLIED
```
public static final int MATRIX_SUPPLIED
```
    use explicit cost matrix
    
    See Also:
    
    Constant Field Values
  - TAGS_MATRIX_SOURCE
```
public static final Tag[] TAGS_MATRIX_SOURCE
```
    Specify possible sources of the cost matrix
- Constructor Detail
  - CostSensitiveClassifier
```
public CostSensitiveClassifier()
```
    Default constructor.
- Method Detail
  - listOptions
```
public java.util.Enumeration<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class RandomizableSingleClassifierEnhancer
    
    Returns:
    
    an enumeration of all the available options.
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Parses a given list of options.
    Valid options are:
```
 -M
  Minimize expected misclassification cost. Default is to
  reweight training instances according to costs per class
```
```
 -C <cost file name>
  File name of a cost matrix to use. If this is not supplied,
  a cost matrix will be loaded on demand. The name of the
  on-demand file is the relation name of the training data
  plus ".cost", and the path to the on-demand file is
  specified with the -N option.
```
```
 -N <directory>
  Name of a directory to search for cost files when loading
  costs on demand (default current directory).
```
```
 -cost-matrix <matrix>
  The cost matrix in Matlab single line format.
```
```
 -S <num>
  Random number seed.
  (default 1)
```
```
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
```
```
 -W
  Full name of base classifier.
  (default: weka.classifiers.rules.ZeroR)
```
```
 
 Options specific to classifier weka.classifiers.rules.ZeroR:
 
```
```
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
```
    Options after -- are passed to the designated classifier.
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class RandomizableSingleClassifierEnhancer
    
    Parameters:
    
    options - the list of options as an array of strings
    
    Throws:
    
    java.lang.Exception - if an option is not supported
  - getOptions
```
public java.lang.String[] getOptions()
```
    Gets the current settings of the Classifier.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class RandomizableSingleClassifierEnhancer
    
    Returns:
    
    an array of strings suitable for passing to setOptions
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns:
    
    a description of the classifier suitable for displaying in the explorer/experimenter gui
  - costMatrixSourceTipText
```
public java.lang.String costMatrixSourceTipText()
```
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getCostMatrixSource
```
public SelectedTag getCostMatrixSource()
```
    Gets the source location method of the cost matrix. Will be one of MATRIX_ON_DEMAND or MATRIX_SUPPLIED.
    
    Returns:
    
    the cost matrix source.
  - setCostMatrixSource
```
public void setCostMatrixSource(SelectedTag newMethod)
```
    Sets the source location of the cost matrix. Values other than MATRIX_ON_DEMAND or MATRIX_SUPPLIED will be ignored.
    
    Parameters:
    
    newMethod - the cost matrix location method.
  - onDemandDirectoryTipText
```
public java.lang.String onDemandDirectoryTipText()
```
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getOnDemandDirectory
```
public java.io.File getOnDemandDirectory()
```
    Returns the directory that will be searched for cost files when loading on demand.
    
    Returns:
    
    The cost file search directory.
  - setOnDemandDirectory
```
public void setOnDemandDirectory(java.io.File newDir)
```
    Sets the directory that will be searched for cost files when loading on demand.
    
    Parameters:
    
    newDir - The cost file search directory.
  - minimizeExpectedCostTipText
```
public java.lang.String minimizeExpectedCostTipText()
```
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getMinimizeExpectedCost
```
public boolean getMinimizeExpectedCost()
```
    Gets the value of MinimizeExpectedCost.
    
    Returns:
    
    Value of MinimizeExpectedCost.
  - setMinimizeExpectedCost
```
public void setMinimizeExpectedCost(boolean newMinimizeExpectedCost)
```
    Set the value of MinimizeExpectedCost.
    
    Parameters:
    
    newMinimizeExpectedCost - Value to assign to MinimizeExpectedCost.
  - costMatrixTipText
```
public java.lang.String costMatrixTipText()
```
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getCostMatrix
```
public CostMatrix getCostMatrix()
```
    Gets the misclassification cost matrix.
    
    Returns:
    
    the cost matrix
  - setCostMatrix
```
public void setCostMatrix(CostMatrix newCostMatrix)
```
    Sets the misclassification cost matrix.
    
    Parameters:
    
    newCostMatrix - the cost matrix
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface Classifier
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class SingleClassifierEnhancer
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws java.lang.Exception
```
    Builds the model of the base learner.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    
    data - the training data
    
    Throws:
    
    java.lang.Exception - if the classifier could not be built successfully
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception
```
    Returns class probabilities. When minimum expected cost approach is chosen, returns probability one for class with the minimum expected misclassification cost. Otherwise it returns the probability distribution returned by the base classifier.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    the computed distribution for the given instance
    
    Throws:
    
    java.lang.Exception - if instance could not be classified successfully
  - distributionsForInstances
```
public double[][] distributionsForInstances(Instances insts)
                                     throws java.lang.Exception
```
    Batch scoring method. Calls the appropriate method for the base learner if it implements BatchPredictor. Otherwise it simply calls the distributionForInstance() method repeatedly.
    
    Specified by:
    
    distributionsForInstances in interface BatchPredictor
    
    Overrides:
    
    distributionsForInstances in class AbstractClassifier
    
    Parameters:
    
    insts - the instances to get predictions for
    
    Returns:
    
    an array of probability distributions, one for each instance
    
    Throws:
    
    java.lang.Exception - if a problem occurs
  - batchSizeTipText
```
public java.lang.String batchSizeTipText()
```
    Tool tip text for this property
    
    Overrides:
    
    batchSizeTipText in class AbstractClassifier
    
    Returns:
    
    the tool tip for this property
  - setBatchSize
```
public void setBatchSize(java.lang.String size)
```
    Set the batch size to use. Gets passed through to the base learner if it implements BatchPredictor. Otherwise it is just ignored.
    
    Specified by:
    
    setBatchSize in interface BatchPredictor
    
    Overrides:
    
    setBatchSize in class AbstractClassifier
    
    Parameters:
    
    size - the batch size to use
  - getBatchSize
```
public java.lang.String getBatchSize()
```
    Gets the preferred batch size from the base learner if it implements BatchPredictor. Returns 1 as the preferred batch size otherwise.
    
    Specified by:
    
    getBatchSize in interface BatchPredictor
    
    Overrides:
    
    getBatchSize in class AbstractClassifier
    
    Returns:
    
    the batch size to use
  - implementsMoreEfficientBatchPrediction
```
public boolean implementsMoreEfficientBatchPrediction()
```
    Returns true if the base classifier implements BatchPredictor and is able to generate batch predictions efficiently
    
    Specified by:
    
    implementsMoreEfficientBatchPrediction in interface BatchPredictor
    
    Overrides:
    
    implementsMoreEfficientBatchPrediction in class AbstractClassifier
    
    Returns:
    
    true if the base classifier can generate batch predictions efficiently
  - graphType
```
public int graphType()
```
    Returns the type of graph this classifier represents.
    
    Specified by:
    
    graphType in interface Drawable
    
    Returns:
    
    the type of graph this classifier represents
  - graph
```
public java.lang.String graph()
                       throws java.lang.Exception
```
    Returns graph describing the classifier (if possible).
    
    Specified by:
    
    graph in interface Drawable
    
    Returns:
    
    the graph of the classifier in dotty format
    
    Throws:
    
    java.lang.Exception - if the classifier cannot be graphed
  - toString
```
public java.lang.String toString()
```
    Output a representation of this classifier
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string representation of the classifier
  - getRevision
```
public java.lang.String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
    
    Returns:
    
    the revision
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - should contain the following arguments: -t training file [-T test file] [-c class index]

Class CostSensitiveClassifier

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Fields inherited from interface weka.core.Drawable

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Field Detail

MATRIX_ON_DEMAND

MATRIX_SUPPLIED

TAGS_MATRIX_SOURCE

Constructor Detail

CostSensitiveClassifier

Method Detail

listOptions

setOptions

getOptions

globalInfo

costMatrixSourceTipText

getCostMatrixSource

setCostMatrixSource

onDemandDirectoryTipText

getOnDemandDirectory

setOnDemandDirectory

minimizeExpectedCostTipText

getMinimizeExpectedCost

setMinimizeExpectedCost

costMatrixTipText

getCostMatrix

setCostMatrix

getCapabilities

buildClassifier

distributionForInstance

distributionsForInstances

batchSizeTipText

setBatchSize

getBatchSize

implementsMoreEfficientBatchPrediction

graphType

graph

toString

getRevision

main